Various types of special-purpose processors, such as graphics processing units (GPUs) for general purpose computing and other types of hardware accelerators, have been developed for accelerated processing of specific types of workloads. The processing capabilities of GPU devices and other types of hardware accelerators are currently being utilized in various applications to accelerate the processing of highly-parallelized computational workloads in various technical fields. In particular, general-purpose computing on GPU (GPGPU) is utilized for high-throughput, accelerated processing of compute kernels for workloads (e.g., vector-based computations, matrix-based computations, etc.) that exhibit data-parallelism. For example, GPUs are used to accelerate data processing in high-performance computing (HPC) and embedded computing systems, for various applications such as financial modeling, scientific research, machine learning (ML), deep learning (DL), data mining, video data transcoding, image analysis, image recognition, virus pattern matching, augmented reality, encryption/decryption, weather forecasting, big data analytics and comparisons, and other applications with computational workloads that have an inherently parallel nature.
A distributed computing environment which comprises a large scale of shared computing resources over a cluster of computing nodes is typically utilized to support emerging applications such as big data analytics and deep learning applications. Indeed, deep learning applications, for example, require the collection, storage, and processing of a significantly large amount of data, wherein the data includes training data to build and optimize deep learning models, as well as model parameters of the deep learning models which are utilized for inference processing. Implementing an efficient distributed computing environment for these types of applications is not trivial as the intensive computational workloads, and the massive volume of data that must be stored, streamed, prefetched, and coordinated between the shared computing resources of the distributed computing platform presents a significant challenge and practical limit on system performance and scalability.